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<p>Rapid urbanization has increased the complexity of managing modern urban infrastructure, has increased the need for intelligent, efficient, and sustainable approach, it needs AI-Cloud integrated technology for intelligent systems capable of optimizing infrastructure, reducing energy utilization, traffic congestion, and fault response time. This study presents an AI–Cloud integrated smart city system designed to unify AI and Cloud Computing technologies for efficient urban infrastructure management. This work implemented using Python-based AI frameworks integrated with AWS cloud infrastructure to evaluate the performance of the proposed AI–Cloud architecture. This paper also provides as unified model repository with lifecycle management. The proposed AI–Cloud framework improves energy efficiency, reduces traffic congestion, accelerates fault response, and enhances scalability and resource utilization in real-world smart city environments, and demonstrated superior performance across urban service domains, achieving improved efficiency and scalability with a 28% gain in energy utilization, a 35% reduction in traffic congestion, and a 40% faster response time compared to conventional systems. These results highlight its effectiveness in enabling sustainable and intelligent next-generation smart city infrastructure.</p>

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AI-Cloud for next generation smart cites and smarter infrastructure

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月11日

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  • Sam Goundar,
  • Basetty Mallikarjuna,
  • Puspalatha Chittem Setty

摘要

Rapid urbanization has increased the complexity of managing modern urban infrastructure, has increased the need for intelligent, efficient, and sustainable approach, it needs AI-Cloud integrated technology for intelligent systems capable of optimizing infrastructure, reducing energy utilization, traffic congestion, and fault response time. This study presents an AI–Cloud integrated smart city system designed to unify AI and Cloud Computing technologies for efficient urban infrastructure management. This work implemented using Python-based AI frameworks integrated with AWS cloud infrastructure to evaluate the performance of the proposed AI–Cloud architecture. This paper also provides as unified model repository with lifecycle management. The proposed AI–Cloud framework improves energy efficiency, reduces traffic congestion, accelerates fault response, and enhances scalability and resource utilization in real-world smart city environments, and demonstrated superior performance across urban service domains, achieving improved efficiency and scalability with a 28% gain in energy utilization, a 35% reduction in traffic congestion, and a 40% faster response time compared to conventional systems. These results highlight its effectiveness in enabling sustainable and intelligent next-generation smart city infrastructure.